Clust
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clustering evaluation framework
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k-Medoids (PAM)
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Best Parameters
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Best Qualities
Best Parameters
Hints:
Which parameter sets lead to the optimal clustering quality?
Please choose a clustering quality measure:
Davies Bouldin Index (R)
Dunn Index (R)
F1-Score
F2-Score
False Discovery Rate
False Positive Rate
Fowlkes Mallows Index (R)
Jaccard Index (R)
Rand Index
Rand Index (R)
Sensitivity
Silhouette Value (R)
Specificity
V-Measure
Dataset
Best quality
Parameter set
brown
1.0
k=216
chang_pathbased
1.0
k=95
ppi_mips
1.0
k=1046
chang_spiral
1.0
k=88
astral_40_strsim
1.0
k=435
astral_40_seqsim_beh
1.0
k=871
fraenti_s3
0.999
k=217
bone_marrow_fixLabels
1.0
k=37
fu_flame
1.0
k=239
coli_state
1.0
k=170
coli_find
1.0
k=414
coli_need
1.0
k=103
coli_time
1.0
k=506
gionis_aggregation
1.0
k=567
veenman_r15
1.0
k=483
zahn_compound
1.0
k=398
synthetic_spirals
1.0
k=194
synthetic_cassini
1.0
k=132
twonorm_100d
1.0
k=198
twonorm_50d
1.0
k=194
synthetic_cuboid
1.0
k=139
astral1_161
1.0
k=288
tcga
1.0
k=241
bone_marrow
1.0
k=33
zachary
1.0
k=9